In [1]:

    

import seldon.pipeline.auto_transforms as auto
import pandas as pd
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)

df = pd.read_csv("http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data",
                 header=None,names=["target","Alcohol","Malic acid","Ash","Alcalinity of ash","Magnesium","Total phenols","Flavanoids",
                         "Nonflavanoid phenols","Proanthocyanins","Color intensity","Hue","OD280/OD315 of diluted wines",
                         "Proline"])


    

In [2]:

    

df.head()


    

    
Out[2]:






  

    

      

      
target
      
Alcohol
      
Malic acid
      
Ash
      
Alcalinity of ash
      
Magnesium
      
Total phenols
      
Flavanoids
      
Nonflavanoid phenols
      
Proanthocyanins
      
Color intensity
      
Hue
      
OD280/OD315 of diluted wines
      
Proline
    
  
  

    

      
0
      
1
      
14.23
      
1.71
      
2.43
      
15.6
      
127
      
2.80
      
3.06
      
0.28
      
2.29
      
5.64
      
1.04
      
3.92
      
1065
    
    

      
1
      
1
      
13.20
      
1.78
      
2.14
      
11.2
      
100
      
2.65
      
2.76
      
0.26
      
1.28
      
4.38
      
1.05
      
3.40
      
1050
    
    

      
2
      
1
      
13.16
      
2.36
      
2.67
      
18.6
      
101
      
2.80
      
3.24
      
0.30
      
2.81
      
5.68
      
1.03
      
3.17
      
1185
    
    

      
3
      
1
      
14.37
      
1.95
      
2.50
      
16.8
      
113
      
3.85
      
3.49
      
0.24
      
2.18
      
7.80
      
0.86
      
3.45
      
1480
    
    

      
4
      
1
      
13.24
      
2.59
      
2.87
      
21.0
      
118
      
2.80
      
2.69
      
0.39
      
1.82
      
4.32
      
1.04
      
2.93
      
735
    
  

In [3]:

    

df["target"] = df["target"] - 1
t_auto = auto.Auto_transform(exclude=["target"])
df2 = t_auto.fit_transform(df)
df2.head()


    

    
Out[3]:






  

    

      

      
target
      
Alcohol
      
Malic acid
      
Ash
      
Alcalinity of ash
      
Magnesium
      
Total phenols
      
Flavanoids
      
Nonflavanoid phenols
      
Proanthocyanins
      
Color intensity
      
Hue
      
OD280/OD315 of diluted wines
      
Proline
    
  
  

    

      
0
      
0
      
1.518613
      
-0.562250
      
0.232053
      
-1.169593
      
1.913905
      
0.808997
      
1.034819
      
-0.659563
      
1.224884
      
0.251717
      
0.362177
      
1.847920
      
1.013009
    
    

      
1
      
0
      
0.246290
      
-0.499413
      
-0.827996
      
-2.490847
      
0.018145
      
0.568648
      
0.733629
      
-0.820719
      
-0.544721
      
-0.293321
      
0.406051
      
1.113449
      
0.965242
    
    

      
2
      
0
      
0.196879
      
0.021231
      
1.109334
      
-0.268738
      
0.088358
      
0.808997
      
1.215533
      
-0.498407
      
2.135968
      
0.269020
      
0.318304
      
0.788587
      
1.395148
    
    

      
3
      
0
      
1.691550
      
-0.346811
      
0.487926
      
-0.809251
      
0.930918
      
2.491446
      
1.466525
      
-0.981875
      
1.032155
      
1.186068
      
-0.427544
      
1.184071
      
2.334574
    
    

      
4
      
0
      
0.295700
      
0.227694
      
1.840403
      
0.451946
      
1.281985
      
0.808997
      
0.663351
      
0.226796
      
0.401404
      
-0.319276
      
0.362177
      
0.449601
      
-0.037874
    
  

In [4]:

    

from seldon import xgb
import seldon.pipeline.cross_validation as cf
xgb = xgb.XGBoostClassifier(target="target")
cv = cf.Seldon_KFold(xgb,5)
cv.fit(df2)


    

    




/home/clive/tools/scikit-learn/sklearn/cross_validation.py:42: DeprecationWarning: This module has been deprecated in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
  "This module will be removed in 0.20.", DeprecationWarning)






    
Out[4]:





Seldon_KFold(clf=XGBoostClassifier(base_score=0.5,
         clf=XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=1,
       gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=3,
       min_child_weight=1, missing=None, n_estimators=100, nthread=-1,
       objective='multi:softprob', reg_al...torizer=DictVectorizer(dtype=<type 'numpy.float64'>, separator='=', sort=True,
        sparse=True)),
       k=5)

In [5]:

    

print "Average accuracy ",cv.get_score()


    

    




Average accuracy  0.96619047619

In [ ]: